{"id":38841,"date":"2023-01-19T14:59:52","date_gmt":"2023-01-19T03:59:52","guid":{"rendered":"https:\/\/www.institutedata.com\/?p=38841"},"modified":"2023-03-09T13:11:58","modified_gmt":"2023-03-09T02:11:58","slug":"beginner-data-scientist-common-mistakes","status":"publish","type":"post","link":"https:\/\/www.institutedata.com\/nz\/blog\/beginner-data-scientist-common-mistakes\/","title":{"rendered":"10 mistakes commonly made by beginner data scientists"},"content":{"rendered":"

Starting is sometimes the most challenging part. In the beginning, it is when habits are formed that can have lasting repercussions on the work being undertaken. Therefore, it is excellent practice to understand early as a beginner data scientist what should be avoided; data science is no exception.<\/span><\/p>\n

Mistakes can be fantastic learning opportunities, but this can only be understood when there is a willingness to grow and a desire to improve continuously. We can overcome wrong moves and turn them into a competitive edge through experimenting and researching. The following article looks at ten mistakes beginner\u00a0<\/span>data scientists<\/span><\/a>\u00a0can make and how they can be avoided.\u00a0<\/span><\/p>\n

MISTAKE #1: Not knowing the basics
\n\"Data<\/span><\/h2>\n

It is not uncommon for data scientists to get caught up in future technology. It\u2019s a driving force for many who enter the tech industry. They are wooed by the idea of self-driving cars, computer vision, and sophisticated robotics, but just like anyone on top of their field – you must first learn to walk before you learn to run.\u00a0<\/span><\/p>\n

It is essential not to get ahead of yourself.\u00a0<\/span>Machine learning<\/span><\/a>\u00a0models need to be understood inside and out with a detailed knowledge of their critical components. How do they work and behave, and how do those mannerisms change when the data does?<\/p>\n

Understanding how an algorithm works and how it can be modified helps when it is necessary to build on existing technologies. Comprehensive knowledge of mathematics, statistics, and machine learning is a must-have.\u00a0<\/span><\/p>\n

MISTAKE #2 Too much code<\/span>
\n<\/span><\/h2>\n

\"A<\/p>\n

One mistake many beginners fall into the trap of is coding too much. Coding excess algorithms is an unnecessary use of time. It is an excellent practice to code from scratch, but it is more critical to know how to apply the correct algorithm in the right way and suitable setting. Time is better spent understanding machine learning algorithms and their strengths and weaknesses.<\/p>\n

MISTAKE #3 Prioritising theory over practice<\/span><\/h2>\n

\"a<\/p>\n

A lot of learning happens in data science through trial and error. Focusing on theory can mean that the understanding gained through the experience of writing code and resolving issues can be missed. For data scientists, the theory is essential, but becoming overwhelmed with information won\u2019t make creating algorithms easier.<\/p>\n

Data science is an applied field, meaning the best way to solidify skills is by using them. This can also assist in retaining concepts. Witnessing how what is being learned connects to the real world can be a great motivator that can be lost when taking a research-heavy approach. It is okay not to know everything before starting. You can solve arising problems along the way.<\/p>\n

MISTAKE #4 Putting too much emphasis on degrees<\/span>
\n<\/span><\/h2>\n

\"a<\/p>\n

Many consider the golden ticket to their dream job in data science to be a degree, but as the industry has grown, it\u2019s no longer something that puts one ahead. It is important not to overestimate the value. It is not to say that they don\u2019t boost your chances, but they are also just a part of a whole person.<\/p>\n

Certificates are fantastic if they are a motivator to learn, a tremendous official indicator of progress, and a clear display of a candidate\u2019s willingness to develop their skillsets and improve. Yet, it is essential to remember that competitors may have the same qualifications, so consider what else can give you the edge.<\/p>\n

What can provide a competitive advantage is more than what is listed on a resume, but what is also important is how knowledge and an understanding of concepts can be applied to the real world.<\/p>\n

MISTAKE #5: Failing to study consistently<\/span>
\n<\/span><\/h2>\n

\"A<\/p>\n

Data science is a challenging field, and an often made-mistake by beginners is not being consistent in the learning process. Concepts can be complex or overwhelming, but it is vital not to become distracted or give up when wanting to understand them. Think of learning as a marathon and not a sprint.<\/p>\n

When training for a marathon, the runner prepares in short stints over a long period.<\/p>\n

Study a little bit every day, and the new ideas will become old habits in no time. Set reachable goals and deadlines consistently throughout your career as a data scientist, not just when you are starting out starting. Learn new topics and revise old ones from new perspectives. Stay up-to-date on trends, the latest technology, business information & data visualisations, and storytelling.<\/p>\n

MISTAKE #6: Worrying about the opinion of others<\/span>
\n<\/span><\/h2>\n

\"a<\/p>\n

Don\u2019t get confused or waylaid by the opinions of others. It is excellent to seek advice and hear what others say, but the more opinions are listened to, the more muddied the waters become. Every data science has its own set of opinions and experiences.\u00a0<\/span><\/p>\n

It is vital to keep an open mind to allow you to form your own opinions. Data scientists use the information as a guide or inspiration rather than as the only ideal option. Searching for facts, drawing own conclusions, and validating ideas are crucial skills in a data scientist\u2019s career.<\/span><\/p>\n

MISTAKE #7: Ignoring feature engineering<\/span><\/h2>\n

\"A<\/p>\n

Forgetting to keep feature engineering front of mind is a more data scientist-specific mistake that is essential to remember when building a data model. Ignoring feature engineering would be a blunder as it provides tools that contribute to a positive solution for data.<\/p>\n

However, it requires a cycle of trial and error involving research and interaction with other technicians, like domain experts; that is an art defined by the problem and its complexity.<\/p>\n

When ignored, the process may be quicker, but it is incredibly inefficient. What is meant by this is data scientists process and clean the first variant of a dataset and follow this with quickly run intensive grid searches for optimising the model parameters on a particular task.<\/p>\n

Feature engineering is when more time is given to building predictive features. Superior machine learning practitioners advise putting in the time here rather than in the hours-long wait while the grid search discovers the parameters. As data scientists, the solution may not lie in the tech but in building the correct features.<\/p>\n

MISTAKE #8 Not talking to domain experts<\/span>
\n<\/span><\/h2>\n

\"A<\/p>\n

Data science is regarded as a highly competitive industry, leading to a reluctance to share information and knowledge between scientists. Thinking this way is a mistake as it can result in biased work that only reflects one perspective on the world. Discussing a topic openly and with others is an excellent skill for data scientists.<\/p>\n

Data scientists do not exist in a vacuum. Interacting with domain experts can lead to insights into the data previously missed. Recruiters need to know that you have a vivid network and a willingness to share knowledge, as it benefits the market value of both the employee and the company they work with.<\/p>\n

MISTAKE #9 Not caring about business knowledge<\/span>
\n<\/span><\/h2>\n

\"a<\/p>\n

Too regularly do data scientists get caught up and excited about\u00a0<\/span>data collection<\/span><\/a>\u00a0and not about its application. Applying the same methods to every problem and industry is impossible, and\u00a0<\/span>business acumen development<\/span><\/a>\u00a0is overlooked. Don\u2019t give data the sole decision-making power.<\/p>\n

Consider also how domain knowledge with technical expertise can be helpful to data scientists and how data analysis contributes to the business\u2019s growth and profits.<\/span><\/p>\n

MISTAKE #10 Never starting<\/span><\/h2>\n

\"A<\/p>\n

Spending too much time considering options may lead to dead ends and non-starts. Success in any field, notably technical, is never instantaneous. The first step can be the hardest, but it can also be the most rewarding. Choose a course that strikes an interest and go from there.\u00a0<\/span><\/p>\n

This list is here to guide you while you take your first steps into Data Science which can be a thrilling and rewarding career path that will expose you to new ideas, environments, and people. If you are interested in starting your career as a data scientist,\u00a0<\/span>book a career consultation<\/span><\/a>\u00a0with us today to find out how to best position yourself for a future in this industry.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Starting is sometimes the most challenging part. In the beginning, it is when habits are formed that can have lasting repercussions on the work being undertaken. Therefore, it is excellent practice to understand early as a beginner data scientist what should be avoided; data science is no exception. Mistakes can be fantastic learning opportunities, but…<\/p>\n","protected":false},"author":1,"featured_media":38835,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[138,730,597,612,613],"tags":[159,623,669,732,731],"class_list":["post-38841","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-big-data-data-science-2","category-data-science-nz","category-data-security-nz","category-data-skills-nz","tag-data-science","tag-data-science-4","tag-data-scientist","tag-data-scientist-2","tag-programming-and-data-science-2"],"_links":{"self":[{"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/posts\/38841","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/comments?post=38841"}],"version-history":[{"count":0,"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/posts\/38841\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/media\/38835"}],"wp:attachment":[{"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/media?parent=38841"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/categories?post=38841"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.institutedata.com\/nz\/wp-json\/wp\/v2\/tags?post=38841"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}